The demographic data of the respondents are stated below in the cross-tabulation of the level of education, areas of design experience, specialization based on their education and/or experience, and years of design experience in bridge infrastructure. Education, experience, and specialization are the main variables discussed in this subtopic.

The sample distribution in Figure 1 showed the level of education and the areas of design experience. Of the respondents’ experience in bridge design, about 30 professionals with master’s degrees were counted, whereas those with bachelor’s degrees were eight and three with Ph.D. degrees. Most respondents have experience in building design, with 60 having a bachelor’s degree and 52 having a master’s degree.

Figure 2 depicts the cross-tabulation between respondents’ specialization and years of design experience in bridge infrastructure. The number of professionals who specialize in bridge engineering with an experience of 6 years is 11. Six professionals have more than 6 years but less than 20 years of experience in total. There is also one practicing professional who has more than 20 years of experience in the bridge engineering area.

The role of the multiple criteria frameworks for sustainable urban bridge design was identified through an exploratory factor analysis. The data collected through a questionnaire survey have been cleaned, screened, and checked for data reliability using Cronbach’s alpha (Ekolu and Quainoo, 2019), which should be more than 0.5 (Vinodh and Joy, 2012). For N = 14 factors and a sample size of 204, it had a value of 0.904. This alpha value represents the perfect inter-reliability of the data; hence, the questionnaire study provides reliability for further analysis.

The role of the framework has been explored at the level of importance of factors. The research questions were organized in a Likert-scale system of measurement on a scale of 1–5. The level of importance taken was 1 = not important, 2 = low importance, 3 = neutral, 4 = important, and 5 = essential. The parameters in terms of their importance in the identification of key roles of a multicriteria framework for designing sustainable urban bridges were collected from the literature and then were sent to be rated by the respondents.

Based on the sampled data, the distribution of normality of the data shall be tested to determine its statistical testing method. As a result, the extent of the data distribution can be determined by determining the Shapiro–Wilk (Yap and Sim, 2011; Hanusz et al., 2016) significance level of the p-value and observing the frequency distribution and Q–Q plot. Therefore, based on the values of p = 0.001, from Table 1 test of normality, the sample data significantly deviated from the normally distributed population, which implies that the data were not normally distributed. Furthermore, by observing the skewness and kurtosis values of the descriptive statistics (Table 2 and Table 3) and the Q–Q plot (Figure 3), it can be seen that the data are not normally distributed. The data testing technique would be nonparametric, in which Spearman’s rank-order correlation (Schober et al., 2018) can be used as a statistical testing method.

The Spearman rank-order correlation was employed considering the data are monotonic (the value of one variable would increase or decrease as the value of the other variables increase or decrease), and the distribution of the data was linearly related. Table 3 shows Spearman’s rank-order correlation coefficient since the data variables are preferred to have a significant relationship with each other. The association is measured using the range of r values suggested by Schober et al. (2018) as absolute magnitude of the observed correlation coefficient interpretation, 0.00–0.10, negligible correlation, 0.10–0.39, weak correlation, 0.40–0.69 moderate correlation, 0.70–0.89 strong correlation, and 0.90–1.00 very strong correlation.

Table 3 clearly shows that the eleven variables illustrated showed a 1% significant level with a moderate association in the same direction. For example, there is a significant moderate correlation between technical and technological mitigation and environmental mitigation toward the role of MCF in attaining sustainability (r = 0.696, p = 0.000, N = 204). In addition, there is also a high significance level in the holistic guidance capability of the MCF and its ability to assess the sustainable urban bridge design with a moderately correlated association (r = 0.690, p = 0.000, N = 204). Whereas the other variables that have a weak degree of correlation are, for example, the level of environmental mitigation over the level of holistic guidance of the design practice (r = 0.237, p = 0.001, N = 204).

Identifying the key roles has been analyzed using the principal component analysis (PCA) extraction techniques. PCA extraction analysis helps underline the main role of a large number of factors (Mackiewicz and Ratajczak, 1993; Shrestha 2021) Data can be considered for PCA analysis if the KMO (Kaiser–Meyer–Olkin) measure of sampling is adequate (>0.5) and if Bartlett’s test of sphericity is significant (<0.05) (Abdi and Williams, 2010; Bro and Smilde, 2014). The test for both requirements was acceptable with a KMO = 0.841 and a significance of sphericity of 0.001 (Table 4).

Using the PCA extraction method of factors which loads into common variance are listed below in the communality’s extraction and oblique rotation (Oblimin method) using Kaiser normalization so that it has a better than orthogonal rotation in the sense of generating stable correlations among the loadings (Costello and Osborne, 2005; Gerbing and Hamilton, 1996; Finch, 2011) (Table 5). Most of the correlation coefficients are more than 0.3, and they all are significant factors with a significance value of 0.001, which is within the acceptable range of value (<0.05). The correlation is positive, and it has a direct relationship with factors.

Therefore, the eleven (11) commonalities extracted by using the Oblimin rotation method are reasonable, and the extracted loadings which have a loading value of more than 0.5 are listed in Table 6. The extractions have a minimum of 0.557 and a maximum of 0.800 loadings. The total variance is also explained in Table 7.

The components that explained the total variance of the factors were components 1 and 2, with a cumulative extraction sum of squared loadings (66.658%). This total variance percentage is within the standard value of more than 60% (Mackiewicz and Ratajczak, 1993; Pallant, 2020).

Components with an eigenvalue of more than one that explain the majority of the total variance had key roles in the MCF (Figure 4). The scree plot illustrates the loadings of all 11 factors, although the greater common variance is explained by component 1.

The computation of eigenvalues using a parallel analysis with Monte Carlo PCA (Watkins, 2006) is shown in Figure 5, and it justifies the two-component load less than the result of scree plot findings using the PCA extraction method.

Table 8 explains the pattern matrix of factors in two components, seven of the factors load on component 1 while the remaining factors load to component 2.

The component matrix of Table 9 validates the result since the components strongly correlate with a value of about 0.5 which is less than 0.7, and the self-correlation is greater than the cross-correlation (1.00 > 0.496). The reliability of the component factors for N = 11 factors is measured with a Cronbach’s alpha of 0.915, which is acceptable when it is compared to the standard value of 0.700.

The role of a multicriteria framework for designing a sustainable urban bridge was clearly identified based on the suggestions given by the experts as per the literature’s recommendation presented in Table 10. The key roles are categorized as main roles and sub-roles. The sub-roles were arranged by their level of importance from high–low for each main role.

The benefits of a framework with various qualities are numerous, and it is particularly useful in tackling the issue of sustainability. In a nutshell, a framework is a hypothetical description of a complex entity or process, or it is the essential structure that underpins a system, notion, or literature. However, a multicriteria framework is defined as a framework that incorporates several criteria for determining the relationship between the widespread adoption of difficult-to-understand ideas, concepts, and approaches.

The utilization of an MCF in the design process would mitigate the potential recurring problems for the environment. Resource depletion due to the high consumption of natural resources creates an imbalance in ecology. These resources are depleted without due consideration for their scarcity in the construction of the built environment. The excessive use of resources by exerting energy on manufacture, transport, and assemblage also emits large amounts of carbon dioxide (CO₂), pollutes the natural environment, and drastically changes the climate, affecting humanity (Kibert, 2013).

One of the strangest aspects of humanity is that it consumes a massive amount of resources, even non-renewable ones, leaving traces of pollution in the process. To make matters worse, most resources are consumed by a small number of countries. This indicates that when the bulk of humanity can afford the same spending behaviors, the process will deteriorate (Pacheco-Torgal and Labrincha, 2013).

Sustainability principles would be addressed by using the MCF through mitigating the potentially recurring problems of social, economy, institutional, technical, and technological issues. In the construction and renovation of buildings, there is a growing understanding of the need to use materials with lower social, economic, and environmental consequences (Pedroso, et al., 2017). However, in the case of infrastructure such as urban bridges, there is a lack of knowledge of resource consumption throughout the delivery of such structures. Construction materials are essential in the supply of urban bridges because they are extracted from the natural environment, transported to the construction site, processed, fabricated on-site, and built into the infrastructure.

In regard to technological change, whether it is in the essence of information technology toward construction, construction techniques, equipment, or material advancement for the realization of the infrastructure, the change is rapid and continuous, not as such debatable. However, in 1998, this was not the same; Levinthal states that discussions of technological transformation have provided drastically opposing viewpoints as technological progress should be steady and incremental or technological development as rapid and discontinuous (Levinthal, 1998). This argument is currently changing differently; the process, speed, and extent of change have an impact on the delivery of our infrastructures.

According to Tatum (1988a), the components and elements of construction technology include construction-applied resources (information, skills, equipment, time, energy, tools, etc), materials and permanent equipment resources, construction processes (methods and tasks), and project requirements and constraints (Tatum, 1988a; Tatum, 1988b). There are also major driving factors to consider in the application of construction technology in our built environment: the increasing technical complexity of constructed facilities, owners facing increasing international competition demanding more of designers and constructors, and the size, diversity, and state of technology (Tatum, 1988b). The construction industry’s efficiency and competitiveness can only be increased by transferring and implementing computing and other sophisticated technology (Koskela and Kazi, 2003; Loosemore and Forsythe, 2019).

Continuous advances in sensor and data collection technology have allowed the installation of large monitoring systems on a variety of structures (Pipinato and De Miranda, 2022). Recent advancements in lean construction and BIM-based design and construction management are extremely beneficial to the long-term delivery of infrastructure, whether it is a bridge or a road.

On the other hand, using a multicriteria framework effectively requires a high level of competence, yet doing so will result in easier decisions about how to use resources. A new design theory known as “circularity” seeks to transform the linear model of modern material resource usage into a circular one that is considered healthy and operates in a closed loop by minimizing harmful impacts (Shady, 2018). Design and sustainability concerns will be based on the circular idea. By decoupling economic development from resource use, the circular economy (CE) promotes a more resource-efficient approach. The model is based on several theories, including industrial ecology, regenerative design, performance economy, and biomimicry (Van Stijn and Gruis, 2020). The three principles that constitute the CE model are described as follows: “1) preserve and enhance natural capital by controlling finite stocks and balancing renewable resource flows; 2) optimize resource yields by circulating products, components, and materials at their highest utility at all times in both technical and biological cycles; 3) foster system effectiveness by revealing and designing out negative externalities” (MacArthur, 2013; Mendoza et al., 2017).

The esthetic and visual quality of a bridge can be assessed using a variety of criteria and approaches. It is necessary to identify the basic criteria that will serve as the foundation for the establishment of a multicriteria assessment technique for the evaluation of new and existing bridges. It is necessary to systematize esthetic evaluation criteria and construct a multicriteria analysis approach for use in the design of esthetically pleasing bridges to improve the esthetic quality of bridges (Rozentale and Paeglitis, 2017).

According to Gauvreau et al. (2002), if bridge design is to be regarded as a viable and distinct form of artistic expression, it must reflect the truths that characterize the underlying essence shared by all works of art, regardless of the medium. By extension, for these facts to be incorporated into bridges, they must first be acknowledged in the conceptual frameworks that designers employ to guide their decisions during the design process Gauvreau et al. (2002).

Infrastructure sustainability has received a lot of interest (McCormick et al., 2013). The first way to produce a sustainable design is to optimize energy and material consumption in a specific built environment. The second way is to attempt an alternative new technique that aids in seeing the design objective/context/problem in a way that can be addressed in a systemic approach. The usage of frameworks and models would make the design objectives for sustainable infrastructure more understandable. When technology such as BIM provides a platform capable of capturing dynamic data that affect the performance and level of service in infrastructure projects, the architecture of the framework forms a learning cycle for critical thinking.

According to Pollalis, et al., (2012) the three main components of quality of life are social, sustainable, and affordable. We can obtain a rich quality of life when we create a balance among these three factors that make up the triple bottom line. Through the careful design of the physical environment and its supporting infrastructure, planners, architects, and engineers can improve all three areas of people’s lives.

A multicriteria framework guides toward a holistic view of the design objective and helps assess and evaluate design outputs. A tighter urban grain, a complex hierarchy of street and access patterns, concentration and proximity of residential and commercial buildings to each other, the central core of the city, access to public transportation, a variety of options for walking and cycling, mature tree cover, and landscaping combine to create an attractive, mature, and durable form and character, encompassing most of the prerequisites for a sustainable form of development (França et al., 2017).

Blizzard and Klotz 2012 have formulated a framework for sustainable whole-system design practice. Engineers, architects, planners, and policymakers can use the processes, ideas, and methodologies extracted from the literature in a range of design disciplines, including the design of sustainable cities and infrastructure. Frameworks detailing the full system design process, concepts, and methodologies would facilitate the design process.

The overall design process necessitates an early involvement with highway planning to influence road alignments for the sake of bridge geometry and esthetics (Liu, 2009; Dobbins, 2011). Surface water must be evacuated off the deck, which necessitates a sufficient longitudinal gradient. Changes in the deck’s gradient or horizontal alignment might cause the deck to appear twisted and should be avoided. If the deck must accept a curved horizontal road alignment, we must ensure the radius and superelevation remain consistent throughout the length (Malekly et al., 2010). If the bridge crosses another road, the road below must have an adequate vision through the bridge opening to meet the required overtaking and stopping sight distances. The positions of the piers or abutments may need to be changed to attain the correct sight lines. Furthermore, understanding and incorporating sustainability principles would be essential in executing a sustainable urban bridge design (Maier et al., 2012), while using MCF. Moreover, it also incurs additional inputs to the design work and would demand a facilitating tool or protocol in a sustainable urban bridge design assignment.